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4 Ways Machine Learning Can Improve Marketing Campaigns

Read Time: 4 minutes

Businesses of all sizes know that the key to successfully selling a product or service is how you market it, who you market it to and when you do so. Machine learning and predictive analytics (PA) play an important role in marketing campaigns by using clusters of data to determine factors regarding consumer behavior.

Machine learning systems churn out work processes using large volumes of data, while predictive analytics engines gather data, analyze it and make predictions about future events. With this technology, companies can take a lot of the guesswork out of a marketing initiative and discover what social outlets are the most successful.

After all, Dell Technologies forecasts that companies will use PA in marketing significantly more throughout the rest of the year and onward to 2030. The company suggests the technology will be utilized to improve the consumer experience and make business decisions that will expand the visibility and appeal of certain products. The future is bright for this technology, as businesses are already reducing overhead costs and bolstering their ROI by developing more accurate metrics of what makes a customer tick. Here are four ways machine learning, along with predictive analytics, can have a positive impact on your company’s marketing efforts.

#1: Analyzing Customer Behavior

Machine learning algorithms offer high value for companies thanks to the technology’s ability to compile large sets of data, analyze them and churn out applications that increase their bottom line. Predictive analytics make it possible to predict patterns based on factors such as demographics, consumer behavior and preferences. Based on these patterns, companies can then determine appropriate marketing and branding strategies, which is why the likes of Amazon have been dominating the ecommerce sphere for so long.

Predictive analytics also utilizes propensity models that give accurate predictions about customer behavior, including how likely they are to engage, chances of unsubscribing and propensity to buy. This data can then be used to make decisions regarding what product to market to a specific customer, as PA will give you a strong idea of the success rate of selling a particular product to a particular person.

#2: Recommendation Engines

By analyzing customer behavior with PA, businesses can develop algorithms that make intelligent recommendations using an array of variables regarding shopping habits, product preferences and brand choice. A well-developed recommendation system can continuously alter its settings to meet a user’s preferences, using deep-learning neural networks to learn information about that customer and tailor the shopping experience for them. Doing so will help a business’ customer retention rates, which is valuable for subscription-based models.

Machine learning can also increase a company’s revenue by improving cart value. The key here is to create flexible product suggestion models that use advanced filtering techniques. The most successful recommendation engines use this technology to find opportune times to suggest a product based on factors such as the time of the year or brands the customer has purchased in the past. Plus, filtering techniques can help companies determine the right marketing avenue, whether it be their website, email or social media.

Another essential way of bolstering ROI is by improving customer engagement. While a customer may not always buy an item after going through a catalog for hours, a product may stick in their mind and they could come back to buy it later. Recommendation engines remember their browsing history and ask customers if they’re still interested in a particular item. In other cases, customer engagement leads to higher revenue. This is the case with YouTube, which nets the bulk of its revenue through ads that are directly linked to how much time consumers spend on the site.

#3: Analyzing Trends and Supply Chain Management

Machine learning algorithms can compile data from multiple years and a slew of customers to determine what products sell the best during specific times of the year or in a particular location. A well-oiled predictive forecast will inform you how to proceed with a marketing campaign while also examining external factors that may have changed the market or budding trends a company could jump on.

Another element of ensuring customer satisfaction and brand integrity is improving the supply chain. PA can help the supply chain by touching on areas where demand is slated to be higher, as well as which retail centers and distributors need extra items in stock to meet this demand. The technology can even examine weather conditions, which can help a business choose the best mode of transportation.

With the right analytical tools, companies can make informed decisions regarding production and shipping requirements. Doing so will ultimately bolster the reputation of a brand, thus increasing its position within the industry.

#4: Price Elasticity and Price Optimization

Changing the price of a product has been a manual task until recent years. Now, machine learning is capable of offering price elasticity for a product or service, as evidenced in the hospitality industry with airlines and hotels. This means the price of a product or service can change according to market trends, with machine learning offering self-adjusting price functionalities. The technology now has a role in other industries such as manufacturing and services, as it can scale price optimization depending on changing trends.

Machine learning is being used to examine distribution channels, dividing customers into various groups based on shopping habits and average cart price, time of the year and the product’s role within a company’s pricing strategy. Changing market demands can inform the algorithm of how a product should be priced and adjust accordingly.

As the world becomes increasingly digitized, marketing campaigns are turning to revolutionary technology such as machine learning to boost their ROI and improve the customer experience. Deep-learning neural networks are at the core of this movement as these algorithms are capable of compiling, processing and using data for real-life applications. A marketing campaign without these tools in the 21st century simply won’t be able to compete with its industry counterparts.

If you’re looking to leverage machine learning and predictive analytics in your marketing technique, you will need a seasoned developer to help you achieve your goals. The team at 7T offers machine learning and predictive analytics technologies with experience in the marketing sphere. We’re also well-versed in other emerging technologies, including augmented reality, virtual reality, blockchain, artificial intelligence and natural language processing.

7T is headquartered in Dallas, but we also serve clients in Austin, Houston, and beyond. If you’re ready to discuss your project, we invite you to contact us today.

Reach out to our team today!

Lacey Williams-McGhee

Lacey Williams is a marketing professional and Harvard graduate student living in the great state of Texas. When she's not working at 7T's headquarters, she can be found on the next flight to the Bahamas, hanging out with her husband and fluffy golden retriever, or studying! Lacey earned a B.A. in journalism from Baylor University. Sic 'em!


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